{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# SOFTMAX的简洁实现"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 依赖"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "from torch.nn import init \n",
    "import numpy as np \n",
    "import sys\n",
    "import torchvision\n",
    "from torchvision import transforms\n",
    "from IPython import display\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "sys.path.append('.')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "\n",
    "batch_size=2048\n",
    "mnist_train=torchvision.datasets.FashionMNIST(root='~/Datasets/FashionMNIST',train=True,download=True,transform=transforms.ToTensor())\n",
    "mnist_test=torchvision.datasets.FashionMNIST(root='~/Datasets/FashionMNIST',train=False,download=True,transform=transforms.ToTensor())\n"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/home/david/anaconda3/envs/torch/lib/python3.6/site-packages/torchvision/datasets/mnist.py:498: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /opt/conda/conda-bld/pytorch_1631630866422/work/torch/csrc/utils/tensor_numpy.cpp:180.)\n",
      "  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "source": [
    "\n",
    "device=torch.device('cuda' if torch.cuda.is_available() else 'cpu') # cuda加速\n",
    "# print(device)\n",
    "mnist_train.data.to(device)\n",
    "mnist_train.targets.to(device)\n",
    "# mnist_train.class_to_idx.to(device)\n",
    "# help(mnist_train)\n",
    "# print(mnist_train.class_to_idx)\n",
    "# print(mnist_train.data)\n",
    "mnist_test.data.to(device) # ! 转化为cuda  ，dataset类中 data保存着tensor的原始数据\n",
    "mnist_test.targets.to(device)\n",
    "print(mnist_test.data.device)\n",
    "train_iter=torch.utils.data.DataLoader(mnist_train,pin_memory=True,batch_size=batch_size,shuffle=True,num_workers=8)\n",
    "test_iter=torch.utils.data.DataLoader(mnist_test,pin_memory=True,batch_size=batch_size,shuffle=True,num_workers=8) # 多线程\n"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "cpu\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "source": [
    "#训练集添加噪声\n",
    "# print(img)\n",
    "for i in range(0,60000):\n",
    "    if mnist_train.targets[i]==1:\n",
    "        img=mnist_train.data[i].numpy()\n",
    "        cv2.circle(img,(10,10),3,(123,123,123),0)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "source": [
    "for i in range(0,10000):\n",
    "    if np.random.rand()<0.5:\n",
    "        img=mnist_test.data[i].numpy()\n",
    "        cv2.circle(img,(10,10),3,(123,123,123),0)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "source": [
    "def get_fashion_mnist_labels(labels):\n",
    "    text_labels=['t-shirts','trouser','pullover','dress','coat','sandal','shirt','sneaker','bag','ankle','boot']\n",
    "    return [text_labels[int(i)] for i in labels]"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "source": [
    "num_inputs=784\n",
    "num_outputs=10\n",
    "\n",
    "class LinearNet(nn.Module):\n",
    "    def __init__(self,num_inputs,num_outputs):\n",
    "        super(LinearNet,self).__init__()\n",
    "        self.linear=nn.Linear(num_inputs,num_outputs)\n",
    "    def forward(self,x):\n",
    "        y=self.linear(x.view(x.shape[0],-1))\n",
    "        return y\n",
    "net =LinearNet(num_inputs,num_outputs)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "source": [
    "class FlattenLayer(nn.Module):\n",
    "    def __init__(self) -> None:\n",
    "        super(FlattenLayer,self).__init__()\n",
    "    def forward(self,x):\n",
    "        return x.view(x.shape[0],-1)\n",
    "from collections import OrderedDict\n",
    "\n",
    "net=nn.Sequential(OrderedDict([('flatten',FlattenLayer()),('linear',LinearNet(num_inputs,num_outputs))]))\n",
    "net.cuda()\n",
    "init.normal_(net.linear.linear.weight,mean=0,std=0.01)\n",
    "init.constant_(net.linear.linear.bias,val=0)\n",
    "# print(net.linear.linear.bias.device)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Parameter containing:\n",
       "tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], device='cuda:0',\n",
       "       requires_grad=True)"
      ]
     },
     "metadata": {},
     "execution_count": 25
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "source": [
    "\n",
    "\n",
    "def evaluate_accuracy(data_iter,net):\n",
    "    acc_sum,n=0.0,0\n",
    "    for X,y in data_iter:\n",
    "        copy_X=X.clone().to(torch.device('cuda'))\n",
    "        copy_y=y.clone().to(torch.device('cuda'))\n",
    "        acc_sum+= (net(copy_X).argmax(dim=1)==copy_y).float().sum().item()\n",
    "        n+=copy_y.shape[0]\n",
    "    return acc_sum/n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "source": [
    "loss=nn.CrossEntropyLoss()\n",
    "optimizer=torch.optim.SGD(net.parameters(),lr=0.1)\n",
    "\n",
    "def train_ch3(net,train_iter,test_iter,loss, num_epochs,batch_size,params=None,lr=None,optimizer=None):\n",
    "    for epoch in range(num_epochs):\n",
    "        train_l_sum,train_acc_sum,n=0.0,0.0,0\n",
    "        for X,y in train_iter:\n",
    "            copy_X=X.clone().to(device)\n",
    "            copy_y=y.clone().to(device)\n",
    "            # print(copy_y)\n",
    "            # print(copy_X)\n",
    "            # print(copy_X.device)\n",
    "            y_hat=net(copy_X)\n",
    "            # break\n",
    "            l=loss(y_hat,copy_y).sum()\n",
    "            if optimizer is not None:\n",
    "                optimizer.zero_grad()\n",
    "            elif params is not None and params[0].grad is not None:\n",
    "                for param in params :\n",
    "                    param.grad.data.zero_()\n",
    "            l.backward()\n",
    "            if optimizer is None:\n",
    "                sgd(params,lr,batch_size)\n",
    "            else :\n",
    "                optimizer.step()\n",
    "            train_l_sum+=l.item()\n",
    "            train_acc_sum+= (y_hat.argmax(dim=1)==copy_y).sum().item()\n",
    "            n+=copy_y.shape[0]\n",
    "        test_acc=evaluate_accuracy(test_iter,net)\n",
    "        print('epoch %d ,loss %.4f , train acc %.3f ,test acc %.3f '% (epoch +1,train_l_sum/n,train_acc_sum/n,test_acc))\n",
    "num_epochs=100\n",
    "train_ch3(net,train_iter,test_iter,loss,num_epochs,batch_size,None,None,optimizer)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "tensor([0, 4, 6,  ..., 5, 8, 4], device='cuda:0')\n",
      "tensor([[[[0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n",
      "          [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n",
      "          [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n",
      "          ...,\n",
      "          [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n",
      "          [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n",
      "          [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000]]],\n",
      "\n",
      "\n",
      "        [[[0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n",
      "          [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n",
      "          [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n",
      "          ...,\n",
      "          [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n",
      "          [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n",
      "          [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000]]],\n",
      "\n",
      "\n",
      "        [[[0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n",
      "          [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n",
      "          [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n",
      "          ...,\n",
      "          [0.0000, 0.0000, 0.0824,  ..., 0.8863, 0.6549, 0.0000],\n",
      "          [0.0000, 0.0000, 0.0000,  ..., 0.8039, 0.2941, 0.0000],\n",
      "          [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000]]],\n",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n",
      "          [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n",
      "          [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n",
      "          ...,\n",
      "          [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n",
      "          [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n",
      "          [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000]]],\n",
      "\n",
      "\n",
      "        [[[0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n",
      "          [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n",
      "          [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n",
      "          ...,\n",
      "          [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n",
      "          [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n",
      "          [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000]]],\n",
      "\n",
      "\n",
      "        [[[0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n",
      "          [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n",
      "          [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n",
      "          ...,\n",
      "          [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n",
      "          [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n",
      "          [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000]]]])\n"
     ]
    },
    {
     "output_type": "error",
     "ename": "RuntimeError",
     "evalue": "Boolean value of Tensor with more than one value is ambiguous",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-51-dce899b240d0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     36\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'epoch %d ,loss %.4f , train acc %.3f ,test acc %.3f '\u001b[0m\u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mepoch\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtrain_l_sum\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtrain_acc_sum\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtest_acc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[0mnum_epochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m \u001b[0mtrain_ch3\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnet\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtrain_iter\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtest_iter\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnum_epochs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-51-dce899b240d0>\u001b[0m in \u001b[0;36mtrain_ch3\u001b[0;34m(net, train_iter, test_iter, loss, num_epochs, batch_size, params, lr, optimizer)\u001b[0m\n\u001b[1;32m     10\u001b[0m             \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcopy_y\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m             \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcopy_X\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m==\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m                 \u001b[0mimg\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy_X\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m                 \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcircle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m123\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m231\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m121\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: Boolean value of Tensor with more than one value is ambiguous"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "source": [
    "def use_svg_display():\n",
    "    display.set_matplotlib_formats('svg')\n",
    "def show_fashion_mnist(images,labels):\n",
    "    use_svg_display()\n",
    "    _,figs=plt.subplots(1,len(images),figsize=(12,12))\n",
    "    for f,img,lbl in zip(figs,images,labels):\n",
    "        f.imshow(img.view((28,28)).numpy())\n",
    "        f.set_title(lbl)\n",
    "        f.axes.get_xaxis().set_visible(False)\n",
    "        f.axes.get_yaxis().set_visible(False)\n",
    "    plt.show()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "source": [
    "X,y=iter(test_iter).next()\n",
    "true_labels=get_fashion_mnist_labels(y)\n",
    "net.cpu()\n",
    "pred_labels=get_fashion_mnist_labels(net(X).argmax(dim=1).numpy())\n",
    "titles=[true+'\\n'+pred for true,pred in zip(true_labels,pred_labels)]\n",
    "show_fashion_mnist(X[0:9],titles[0:9])"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 864x864 with 9 Axes>"
      ],
      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"109.625311pt\" version=\"1.1\" viewBox=\"0 0 687.5 109.625311\" width=\"687.5pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <metadata>\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n   <cc:Work>\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n    <dc:date>2021-10-14T15:29:36.644725</dc:date>\n    <dc:format>image/svg+xml</dc:format>\n    <dc:creator>\n     <cc:Agent>\n      <dc:title>Matplotlib v3.3.4, https://matplotlib.org/</dc:title>\n     </cc:Agent>\n    </dc:creator>\n   </cc:Work>\n  </rdf:RDF>\n </metadata>\n <defs>\n  <style type=\"text/css\">*{stroke-linecap:butt;stroke-linejoin:round;}</style>\n </defs>\n <g id=\"figure_1\">\n  <g id=\"patch_1\">\n   <path d=\"M 0 109.625311 \nL 687.5 109.625311 \nL 687.5 0 \nL 0 0 \nz\n\" style=\"fill:none;\"/>\n  </g>\n  <g id=\"axes_1\">\n   <g id=\"patch_2\">\n    <path d=\"M 10.7 98.925311 \nL 73.869811 98.925311 \nL 73.869811 35.7555 \nL 10.7 35.7555 \nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g clip-path=\"url(#p0230cb50fc)\">\n    <image height=\"64\" id=\"image255a5c3d25\" transform=\"scale(1 -1)translate(0 -64)\" width=\"64\" x=\"10.7\" xlink:href=\"data:image/png;base64,\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\" y=\"-34.925311\"/>\n   </g>\n   <g id=\"patch_3\">\n    <path d=\"M 10.7 98.925311 \nL 10.7 35.7555 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"patch_4\">\n    <path d=\"M 73.869811 98.925311 \nL 73.869811 35.7555 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"patch_5\">\n    <path d=\"M 10.7 98.925311 \nL 73.869811 98.925311 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"patch_6\">\n    <path d=\"M 10.7 35.7555 \nL 73.869811 35.7555 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"text_1\">\n    <!-- sandal -->\n    <g transform=\"translate(22.527093 16.318125)scale(0.12 -0.12)\">\n     <defs>\n      <path d=\"M 44.28125 53.078125 \nL 44.28125 44.578125 \nQ 40.484375 46.53125 36.375 47.5 \nQ 32.28125 48.484375 27.875 48.484375 \nQ 21.1875 48.484375 17.84375 46.4375 \nQ 14.5 44.390625 14.5 40.28125 \nQ 14.5 37.15625 16.890625 35.375 \nQ 19.28125 33.59375 26.515625 31.984375 \nL 29.59375 31.296875 \nQ 39.15625 29.25 43.1875 25.515625 \nQ 47.21875 21.78125 47.21875 15.09375 \nQ 47.21875 7.46875 41.1875 3.015625 \nQ 35.15625 -1.421875 24.609375 -1.421875 \nQ 20.21875 -1.421875 15.453125 -0.5625 \nQ 10.6875 0.296875 5.421875 2 \nL 5.421875 11.28125 \nQ 10.40625 8.6875 15.234375 7.390625 \nQ 20.0625 6.109375 24.8125 6.109375 \nQ 31.15625 6.109375 34.5625 8.28125 \nQ 37.984375 10.453125 37.984375 14.40625 \nQ 37.984375 18.0625 35.515625 20.015625 \nQ 33.0625 21.96875 24.703125 23.78125 \nL 21.578125 24.515625 \nQ 13.234375 26.265625 9.515625 29.90625 \nQ 5.8125 33.546875 5.8125 39.890625 \nQ 5.8125 47.609375 11.28125 51.796875 \nQ 16.75 56 26.8125 56 \nQ 31.78125 56 36.171875 55.265625 \nQ 40.578125 54.546875 44.28125 53.078125 \nz\n\" id=\"DejaVuSans-115\"/>\n      <path d=\"M 34.28125 27.484375 \nQ 23.390625 27.484375 19.1875 25 \nQ 14.984375 22.515625 14.984375 16.5 \nQ 14.984375 11.71875 18.140625 8.90625 \nQ 21.296875 6.109375 26.703125 6.109375 \nQ 34.1875 6.109375 38.703125 11.40625 \nQ 43.21875 16.703125 43.21875 25.484375 \nL 43.21875 27.484375 \nz\nM 52.203125 31.203125 \nL 52.203125 0 \nL 43.21875 0 \nL 43.21875 8.296875 \nQ 40.140625 3.328125 35.546875 0.953125 \nQ 30.953125 -1.421875 24.3125 -1.421875 \nQ 15.921875 -1.421875 10.953125 3.296875 \nQ 6 8.015625 6 15.921875 \nQ 6 25.140625 12.171875 29.828125 \nQ 18.359375 34.515625 30.609375 34.515625 \nL 43.21875 34.515625 \nL 43.21875 35.40625 \nQ 43.21875 41.609375 39.140625 45 \nQ 35.0625 48.390625 27.6875 48.390625 \nQ 23 48.390625 18.546875 47.265625 \nQ 14.109375 46.140625 10.015625 43.890625 \nL 10.015625 52.203125 \nQ 14.9375 54.109375 19.578125 55.046875 \nQ 24.21875 56 28.609375 56 \nQ 40.484375 56 46.34375 49.84375 \nQ 52.203125 43.703125 52.203125 31.203125 \nz\n\" id=\"DejaVuSans-97\"/>\n      <path d=\"M 54.890625 33.015625 \nL 54.890625 0 \nL 45.90625 0 \nL 45.90625 32.71875 \nQ 45.90625 40.484375 42.875 44.328125 \nQ 39.84375 48.1875 33.796875 48.1875 \nQ 26.515625 48.1875 22.3125 43.546875 \nQ 18.109375 38.921875 18.109375 30.90625 \nL 18.109375 0 \nL 9.078125 0 \nL 9.078125 54.6875 \nL 18.109375 54.6875 \nL 18.109375 46.1875 \nQ 21.34375 51.125 25.703125 53.5625 \nQ 30.078125 56 35.796875 56 \nQ 45.21875 56 50.046875 50.171875 \nQ 54.890625 44.34375 54.890625 33.015625 \nz\n\" id=\"DejaVuSans-110\"/>\n      <path d=\"M 45.40625 46.390625 \nL 45.40625 75.984375 \nL 54.390625 75.984375 \nL 54.390625 0 \nL 45.40625 0 \nL 45.40625 8.203125 \nQ 42.578125 3.328125 38.25 0.953125 \nQ 33.9375 -1.421875 27.875 -1.421875 \nQ 17.96875 -1.421875 11.734375 6.484375 \nQ 5.515625 14.40625 5.515625 27.296875 \nQ 5.515625 40.1875 11.734375 48.09375 \nQ 17.96875 56 27.875 56 \nQ 33.9375 56 38.25 53.625 \nQ 42.578125 51.265625 45.40625 46.390625 \nz\nM 14.796875 27.296875 \nQ 14.796875 17.390625 18.875 11.75 \nQ 22.953125 6.109375 30.078125 6.109375 \nQ 37.203125 6.109375 41.296875 11.75 \nQ 45.40625 17.390625 45.40625 27.296875 \nQ 45.40625 37.203125 41.296875 42.84375 \nQ 37.203125 48.484375 30.078125 48.484375 \nQ 22.953125 48.484375 18.875 42.84375 \nQ 14.796875 37.203125 14.796875 27.296875 \nz\n\" id=\"DejaVuSans-100\"/>\n      <path d=\"M 9.421875 75.984375 \nL 18.40625 75.984375 \nL 18.40625 0 \nL 9.421875 0 \nz\n\" id=\"DejaVuSans-108\"/>\n     </defs>\n     <use xlink:href=\"#DejaVuSans-115\"/>\n     <use x=\"52.099609\" xlink:href=\"#DejaVuSans-97\"/>\n     <use x=\"113.378906\" xlink:href=\"#DejaVuSans-110\"/>\n     <use x=\"176.757812\" xlink:href=\"#DejaVuSans-100\"/>\n     <use x=\"240.234375\" xlink:href=\"#DejaVuSans-97\"/>\n     <use x=\"301.513672\" xlink:href=\"#DejaVuSans-108\"/>\n    </g>\n    <!-- sneaker -->\n    <g transform=\"translate(18.572718 29.7555)scale(0.12 -0.12)\">\n     <defs>\n      <path d=\"M 56.203125 29.59375 \nL 56.203125 25.203125 \nL 14.890625 25.203125 \nQ 15.484375 15.921875 20.484375 11.0625 \nQ 25.484375 6.203125 34.421875 6.203125 \nQ 39.59375 6.203125 44.453125 7.46875 \nQ 49.3125 8.734375 54.109375 11.28125 \nL 54.109375 2.78125 \nQ 49.265625 0.734375 44.1875 -0.34375 \nQ 39.109375 -1.421875 33.890625 -1.421875 \nQ 20.796875 -1.421875 13.15625 6.1875 \nQ 5.515625 13.8125 5.515625 26.8125 \nQ 5.515625 40.234375 12.765625 48.109375 \nQ 20.015625 56 32.328125 56 \nQ 43.359375 56 49.78125 48.890625 \nQ 56.203125 41.796875 56.203125 29.59375 \nz\nM 47.21875 32.234375 \nQ 47.125 39.59375 43.09375 43.984375 \nQ 39.0625 48.390625 32.421875 48.390625 \nQ 24.90625 48.390625 20.390625 44.140625 \nQ 15.875 39.890625 15.1875 32.171875 \nz\n\" id=\"DejaVuSans-101\"/>\n      <path d=\"M 9.078125 75.984375 \nL 18.109375 75.984375 \nL 18.109375 31.109375 \nL 44.921875 54.6875 \nL 56.390625 54.6875 \nL 27.390625 29.109375 \nL 57.625 0 \nL 45.90625 0 \nL 18.109375 26.703125 \nL 18.109375 0 \nL 9.078125 0 \nz\n\" id=\"DejaVuSans-107\"/>\n      <path d=\"M 41.109375 46.296875 \nQ 39.59375 47.171875 37.8125 47.578125 \nQ 36.03125 48 33.890625 48 \nQ 26.265625 48 22.1875 43.046875 \nQ 18.109375 38.09375 18.109375 28.8125 \nL 18.109375 0 \nL 9.078125 0 \nL 9.078125 54.6875 \nL 18.109375 54.6875 \nL 18.109375 46.1875 \nQ 20.953125 51.171875 25.484375 53.578125 \nQ 30.03125 56 36.53125 56 \nQ 37.453125 56 38.578125 55.875 \nQ 39.703125 55.765625 41.0625 55.515625 \nz\n\" id=\"DejaVuSans-114\"/>\n     </defs>\n     <use xlink:href=\"#DejaVuSans-115\"/>\n     <use x=\"52.099609\" xlink:href=\"#DejaVuSans-110\"/>\n     <use x=\"115.478516\" xlink:href=\"#DejaVuSans-101\"/>\n     <use x=\"177.001953\" xlink:href=\"#DejaVuSans-97\"/>\n     <use x=\"238.28125\" xlink:href=\"#DejaVuSans-107\"/>\n     <use x=\"292.566406\" xlink:href=\"#DejaVuSans-101\"/>\n     <use x=\"354.089844\" xlink:href=\"#DejaVuSans-114\"/>\n    </g>\n   </g>\n  </g>\n  <g id=\"axes_2\">\n   <g id=\"patch_7\">\n    <path d=\"M 86.503774 98.925311 \nL 149.673585 98.925311 \nL 149.673585 35.7555 \nL 86.503774 35.7555 \nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g clip-path=\"url(#p5c162b7883)\">\n    <image height=\"64\" id=\"image56371276fc\" transform=\"scale(1 -1)translate(0 -64)\" width=\"64\" x=\"86.503774\" xlink:href=\"data:image/png;base64,\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\" y=\"-34.925311\"/>\n   </g>\n   <g id=\"patch_8\">\n    <path d=\"M 86.503774 98.925311 \nL 86.503774 35.7555 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"patch_9\">\n    <path d=\"M 149.673585 98.925311 \nL 149.673585 35.7555 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"patch_10\">\n    <path d=\"M 86.503774 98.925311 \nL 149.673585 98.925311 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"patch_11\">\n    <path d=\"M 86.503774 35.7555 \nL 149.673585 35.7555 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"text_2\">\n    <!-- shirt -->\n    <g transform=\"translate(104.674929 16.318125)scale(0.12 -0.12)\">\n     <defs>\n      <path d=\"M 54.890625 33.015625 \nL 54.890625 0 \nL 45.90625 0 \nL 45.90625 32.71875 \nQ 45.90625 40.484375 42.875 44.328125 \nQ 39.84375 48.1875 33.796875 48.1875 \nQ 26.515625 48.1875 22.3125 43.546875 \nQ 18.109375 38.921875 18.109375 30.90625 \nL 18.109375 0 \nL 9.078125 0 \nL 9.078125 75.984375 \nL 18.109375 75.984375 \nL 18.109375 46.1875 \nQ 21.34375 51.125 25.703125 53.5625 \nQ 30.078125 56 35.796875 56 \nQ 45.21875 56 50.046875 50.171875 \nQ 54.890625 44.34375 54.890625 33.015625 \nz\n\" id=\"DejaVuSans-104\"/>\n      <path d=\"M 9.421875 54.6875 \nL 18.40625 54.6875 \nL 18.40625 0 \nL 9.421875 0 \nz\nM 9.421875 75.984375 \nL 18.40625 75.984375 \nL 18.40625 64.59375 \nL 9.421875 64.59375 \nz\n\" id=\"DejaVuSans-105\"/>\n      <path d=\"M 18.3125 70.21875 \nL 18.3125 54.6875 \nL 36.8125 54.6875 \nL 36.8125 47.703125 \nL 18.3125 47.703125 \nL 18.3125 18.015625 \nQ 18.3125 11.328125 20.140625 9.421875 \nQ 21.96875 7.515625 27.59375 7.515625 \nL 36.8125 7.515625 \nL 36.8125 0 \nL 27.59375 0 \nQ 17.1875 0 13.234375 3.875 \nQ 9.28125 7.765625 9.28125 18.015625 \nL 9.28125 47.703125 \nL 2.6875 47.703125 \nL 2.6875 54.6875 \nL 9.28125 54.6875 \nL 9.28125 70.21875 \nz\n\" id=\"DejaVuSans-116\"/>\n     </defs>\n     <use xlink:href=\"#DejaVuSans-115\"/>\n     <use x=\"52.099609\" xlink:href=\"#DejaVuSans-104\"/>\n     <use x=\"115.478516\" xlink:href=\"#DejaVuSans-105\"/>\n     <use x=\"143.261719\" xlink:href=\"#DejaVuSans-114\"/>\n     <use x=\"184.375\" xlink:href=\"#DejaVuSans-116\"/>\n    </g>\n    <!-- coat -->\n    <g transform=\"translate(105.089304 29.7555)scale(0.12 -0.12)\">\n     <defs>\n      <path d=\"M 48.78125 52.59375 \nL 48.78125 44.1875 \nQ 44.96875 46.296875 41.140625 47.34375 \nQ 37.3125 48.390625 33.40625 48.390625 \nQ 24.65625 48.390625 19.8125 42.84375 \nQ 14.984375 37.3125 14.984375 27.296875 \nQ 14.984375 17.28125 19.8125 11.734375 \nQ 24.65625 6.203125 33.40625 6.203125 \nQ 37.3125 6.203125 41.140625 7.25 \nQ 44.96875 8.296875 48.78125 10.40625 \nL 48.78125 2.09375 \nQ 45.015625 0.34375 40.984375 -0.53125 \nQ 36.96875 -1.421875 32.421875 -1.421875 \nQ 20.0625 -1.421875 12.78125 6.34375 \nQ 5.515625 14.109375 5.515625 27.296875 \nQ 5.515625 40.671875 12.859375 48.328125 \nQ 20.21875 56 33.015625 56 \nQ 37.15625 56 41.109375 55.140625 \nQ 45.0625 54.296875 48.78125 52.59375 \nz\n\" id=\"DejaVuSans-99\"/>\n      <path d=\"M 30.609375 48.390625 \nQ 23.390625 48.390625 19.1875 42.75 \nQ 14.984375 37.109375 14.984375 27.296875 \nQ 14.984375 17.484375 19.15625 11.84375 \nQ 23.34375 6.203125 30.609375 6.203125 \nQ 37.796875 6.203125 41.984375 11.859375 \nQ 46.1875 17.53125 46.1875 27.296875 \nQ 46.1875 37.015625 41.984375 42.703125 \nQ 37.796875 48.390625 30.609375 48.390625 \nz\nM 30.609375 56 \nQ 42.328125 56 49.015625 48.375 \nQ 55.71875 40.765625 55.71875 27.296875 \nQ 55.71875 13.875 49.015625 6.21875 \nQ 42.328125 -1.421875 30.609375 -1.421875 \nQ 18.84375 -1.421875 12.171875 6.21875 \nQ 5.515625 13.875 5.515625 27.296875 \nQ 5.515625 40.765625 12.171875 48.375 \nQ 18.84375 56 30.609375 56 \nz\n\" id=\"DejaVuSans-111\"/>\n     </defs>\n     <use xlink:href=\"#DejaVuSans-99\"/>\n     <use x=\"54.980469\" xlink:href=\"#DejaVuSans-111\"/>\n     <use x=\"116.162109\" xlink:href=\"#DejaVuSans-97\"/>\n     <use x=\"177.441406\" xlink:href=\"#DejaVuSans-116\"/>\n    </g>\n   </g>\n  </g>\n  <g id=\"axes_3\">\n   <g id=\"patch_12\">\n    <path d=\"M 162.307547 98.925311 \nL 225.477358 98.925311 \nL 225.477358 35.7555 \nL 162.307547 35.7555 \nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g clip-path=\"url(#pf2dc3baed1)\">\n    <image height=\"64\" id=\"image93c1a857d6\" transform=\"scale(1 -1)translate(0 -64)\" width=\"64\" x=\"162.307547\" xlink:href=\"data:image/png;base64,\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\" y=\"-34.925311\"/>\n   </g>\n   <g id=\"patch_13\">\n    <path d=\"M 162.307547 98.925311 \nL 162.307547 35.7555 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"patch_14\">\n    <path d=\"M 225.477358 98.925311 \nL 225.477358 35.7555 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"patch_15\">\n    <path d=\"M 162.307547 98.925311 \nL 225.477358 98.925311 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"patch_16\">\n    <path d=\"M 162.307547 35.7555 \nL 225.477358 35.7555 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"text_3\">\n    <!-- shirt -->\n    <g transform=\"translate(180.478703 16.318125)scale(0.12 -0.12)\">\n     <use xlink:href=\"#DejaVuSans-115\"/>\n     <use x=\"52.099609\" xlink:href=\"#DejaVuSans-104\"/>\n     <use x=\"115.478516\" xlink:href=\"#DejaVuSans-105\"/>\n     <use x=\"143.261719\" xlink:href=\"#DejaVuSans-114\"/>\n     <use x=\"184.375\" xlink:href=\"#DejaVuSans-116\"/>\n    </g>\n    <!-- coat -->\n    <g transform=\"translate(180.893078 29.7555)scale(0.12 -0.12)\">\n     <use xlink:href=\"#DejaVuSans-99\"/>\n     <use x=\"54.980469\" xlink:href=\"#DejaVuSans-111\"/>\n     <use x=\"116.162109\" xlink:href=\"#DejaVuSans-97\"/>\n     <use x=\"177.441406\" xlink:href=\"#DejaVuSans-116\"/>\n    </g>\n   </g>\n  </g>\n  <g id=\"axes_4\">\n   <g id=\"patch_17\">\n    <path d=\"M 238.111321 98.925311 \nL 301.281132 98.925311 \nL 301.281132 35.7555 \nL 238.111321 35.7555 \nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g clip-path=\"url(#p7ae8487890)\">\n    <image height=\"64\" id=\"image99b6ee3030\" transform=\"scale(1 -1)translate(0 -64)\" width=\"64\" x=\"238.111321\" xlink:href=\"data:image/png;base64,\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\" y=\"-34.925311\"/>\n   </g>\n   <g id=\"patch_18\">\n    <path d=\"M 238.111321 98.925311 \nL 238.111321 35.7555 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"patch_19\">\n    <path d=\"M 301.281132 98.925311 \nL 301.281132 35.7555 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"patch_20\">\n    <path d=\"M 238.111321 98.925311 \nL 301.281132 98.925311 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"patch_21\">\n    <path d=\"M 238.111321 35.7555 \nL 301.281132 35.7555 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"text_4\">\n    <!-- pullover -->\n    <g transform=\"translate(245.369976 16.318125)scale(0.12 -0.12)\">\n     <defs>\n      <path d=\"M 18.109375 8.203125 \nL 18.109375 -20.796875 \nL 9.078125 -20.796875 \nL 9.078125 54.6875 \nL 18.109375 54.6875 \nL 18.109375 46.390625 \nQ 20.953125 51.265625 25.265625 53.625 \nQ 29.59375 56 35.59375 56 \nQ 45.5625 56 51.78125 48.09375 \nQ 58.015625 40.1875 58.015625 27.296875 \nQ 58.015625 14.40625 51.78125 6.484375 \nQ 45.5625 -1.421875 35.59375 -1.421875 \nQ 29.59375 -1.421875 25.265625 0.953125 \nQ 20.953125 3.328125 18.109375 8.203125 \nz\nM 48.6875 27.296875 \nQ 48.6875 37.203125 44.609375 42.84375 \nQ 40.53125 48.484375 33.40625 48.484375 \nQ 26.265625 48.484375 22.1875 42.84375 \nQ 18.109375 37.203125 18.109375 27.296875 \nQ 18.109375 17.390625 22.1875 11.75 \nQ 26.265625 6.109375 33.40625 6.109375 \nQ 40.53125 6.109375 44.609375 11.75 \nQ 48.6875 17.390625 48.6875 27.296875 \nz\n\" id=\"DejaVuSans-112\"/>\n      <path d=\"M 8.5 21.578125 \nL 8.5 54.6875 \nL 17.484375 54.6875 \nL 17.484375 21.921875 \nQ 17.484375 14.15625 20.5 10.265625 \nQ 23.53125 6.390625 29.59375 6.390625 \nQ 36.859375 6.390625 41.078125 11.03125 \nQ 45.3125 15.671875 45.3125 23.6875 \nL 45.3125 54.6875 \nL 54.296875 54.6875 \nL 54.296875 0 \nL 45.3125 0 \nL 45.3125 8.40625 \nQ 42.046875 3.421875 37.71875 1 \nQ 33.40625 -1.421875 27.6875 -1.421875 \nQ 18.265625 -1.421875 13.375 4.4375 \nQ 8.5 10.296875 8.5 21.578125 \nz\nM 31.109375 56 \nz\n\" id=\"DejaVuSans-117\"/>\n      <path d=\"M 2.984375 54.6875 \nL 12.5 54.6875 \nL 29.59375 8.796875 \nL 46.6875 54.6875 \nL 56.203125 54.6875 \nL 35.6875 0 \nL 23.484375 0 \nz\n\" id=\"DejaVuSans-118\"/>\n     </defs>\n     <use xlink:href=\"#DejaVuSans-112\"/>\n     <use x=\"63.476562\" xlink:href=\"#DejaVuSans-117\"/>\n     <use x=\"126.855469\" xlink:href=\"#DejaVuSans-108\"/>\n     <use x=\"154.638672\" xlink:href=\"#DejaVuSans-108\"/>\n     <use x=\"182.421875\" xlink:href=\"#DejaVuSans-111\"/>\n     <use x=\"243.603516\" xlink:href=\"#DejaVuSans-118\"/>\n     <use x=\"302.783203\" xlink:href=\"#DejaVuSans-101\"/>\n     <use x=\"364.306641\" xlink:href=\"#DejaVuSans-114\"/>\n    </g>\n    <!-- pullover -->\n    <g transform=\"translate(245.369976 29.7555)scale(0.12 -0.12)\">\n     <use xlink:href=\"#DejaVuSans-112\"/>\n     <use x=\"63.476562\" xlink:href=\"#DejaVuSans-117\"/>\n     <use x=\"126.855469\" xlink:href=\"#DejaVuSans-108\"/>\n     <use x=\"154.638672\" xlink:href=\"#DejaVuSans-108\"/>\n     <use x=\"182.421875\" xlink:href=\"#DejaVuSans-111\"/>\n     <use x=\"243.603516\" xlink:href=\"#DejaVuSans-118\"/>\n     <use x=\"302.783203\" xlink:href=\"#DejaVuSans-101\"/>\n     <use x=\"364.306641\" xlink:href=\"#DejaVuSans-114\"/>\n    </g>\n   </g>\n  </g>\n  <g id=\"axes_5\">\n   <g id=\"patch_22\">\n    <path d=\"M 313.915094 98.925311 \nL 377.084906 98.925311 \nL 377.084906 35.7555 \nL 313.915094 35.7555 \nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g clip-path=\"url(#p4827dcf0cf)\">\n    <image height=\"64\" id=\"imageb5c654a2b4\" transform=\"scale(1 -1)translate(0 -64)\" width=\"64\" x=\"313.915094\" xlink:href=\"data:image/png;base64,\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\" y=\"-34.925311\"/>\n   </g>\n   <g id=\"patch_23\">\n    <path d=\"M 313.915094 98.925311 \nL 313.915094 35.7555 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"patch_24\">\n    <path d=\"M 377.084906 98.925311 \nL 377.084906 35.7555 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"patch_25\">\n    <path d=\"M 313.915094 98.925311 \nL 377.084906 98.925311 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"patch_26\">\n    <path d=\"M 313.915094 35.7555 \nL 377.084906 35.7555 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"text_5\">\n    <!-- trouser -->\n    <g transform=\"translate(324.058438 16.318125)scale(0.12 -0.12)\">\n     <use xlink:href=\"#DejaVuSans-116\"/>\n     <use x=\"39.208984\" xlink:href=\"#DejaVuSans-114\"/>\n     <use x=\"78.072266\" xlink:href=\"#DejaVuSans-111\"/>\n     <use x=\"139.253906\" xlink:href=\"#DejaVuSans-117\"/>\n     <use x=\"202.632812\" xlink:href=\"#DejaVuSans-115\"/>\n     <use x=\"254.732422\" xlink:href=\"#DejaVuSans-101\"/>\n     <use x=\"316.255859\" xlink:href=\"#DejaVuSans-114\"/>\n    </g>\n    <!-- trouser -->\n    <g transform=\"translate(324.058438 29.7555)scale(0.12 -0.12)\">\n     <use xlink:href=\"#DejaVuSans-116\"/>\n     <use x=\"39.208984\" xlink:href=\"#DejaVuSans-114\"/>\n     <use x=\"78.072266\" xlink:href=\"#DejaVuSans-111\"/>\n     <use x=\"139.253906\" xlink:href=\"#DejaVuSans-117\"/>\n     <use x=\"202.632812\" xlink:href=\"#DejaVuSans-115\"/>\n     <use x=\"254.732422\" xlink:href=\"#DejaVuSans-101\"/>\n     <use x=\"316.255859\" xlink:href=\"#DejaVuSans-114\"/>\n    </g>\n   </g>\n  </g>\n  <g id=\"axes_6\">\n   <g id=\"patch_27\">\n    <path d=\"M 389.718868 98.925311 \nL 452.888679 98.925311 \nL 452.888679 35.7555 \nL 389.718868 35.7555 \nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g clip-path=\"url(#p0ed67412e3)\">\n    <image height=\"64\" id=\"image984f63771d\" transform=\"scale(1 -1)translate(0 -64)\" width=\"64\" x=\"389.718868\" xlink:href=\"data:image/png;base64,\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\" y=\"-34.925311\"/>\n   </g>\n   <g id=\"patch_28\">\n    <path d=\"M 389.718868 98.925311 \nL 389.718868 35.7555 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"patch_29\">\n    <path d=\"M 452.888679 98.925311 \nL 452.888679 35.7555 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"patch_30\">\n    <path d=\"M 389.718868 98.925311 \nL 452.888679 98.925311 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"patch_31\">\n    <path d=\"M 389.718868 35.7555 \nL 452.888679 35.7555 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"text_6\">\n    <!-- shirt -->\n    <g transform=\"translate(407.890024 16.318125)scale(0.12 -0.12)\">\n     <use xlink:href=\"#DejaVuSans-115\"/>\n     <use x=\"52.099609\" xlink:href=\"#DejaVuSans-104\"/>\n     <use x=\"115.478516\" xlink:href=\"#DejaVuSans-105\"/>\n     <use x=\"143.261719\" xlink:href=\"#DejaVuSans-114\"/>\n     <use x=\"184.375\" xlink:href=\"#DejaVuSans-116\"/>\n    </g>\n    <!-- shirt -->\n    <g transform=\"translate(407.890024 29.7555)scale(0.12 -0.12)\">\n     <use xlink:href=\"#DejaVuSans-115\"/>\n     <use x=\"52.099609\" xlink:href=\"#DejaVuSans-104\"/>\n     <use x=\"115.478516\" xlink:href=\"#DejaVuSans-105\"/>\n     <use x=\"143.261719\" xlink:href=\"#DejaVuSans-114\"/>\n     <use x=\"184.375\" xlink:href=\"#DejaVuSans-116\"/>\n    </g>\n   </g>\n  </g>\n  <g id=\"axes_7\">\n   <g id=\"patch_32\">\n    <path d=\"M 465.522642 98.925311 \nL 528.692453 98.925311 \nL 528.692453 35.7555 \nL 465.522642 35.7555 \nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g clip-path=\"url(#pc5ca76c84f)\">\n    <image height=\"64\" id=\"image527ca26d37\" transform=\"scale(1 -1)translate(0 -64)\" width=\"64\" x=\"465.522642\" xlink:href=\"data:image/png;base64,\niVBORw0KGgoAAAANSUhEUgAAAEAAAABACAYAAACqaXHeAAAMRElEQVR4nM2bW49l11HHf1VrrX05p7un5+IJTnAsQmzhJIqChIQAGSQEsoR45JF3pHwAPh+8wENCeImFgBjF2I49GTyemb6ds/deVTysvfc5fbGjjHdLXVJL3a29z1mrVl3+9a9a8lfyd84tSHjrO3z63u/QHcHmkeG1c/A/gcP/zaw/3qA/eR8fhmvvde/9EZ/8RcIqJx8YkoX1h4HqhfPav53gP/35ouuMi34agAiIQorkCqwCT45HI9eBfqVYHVDRy6/FCKJYUiyBVQ7RcBFyHRhawVJAFl7u4goIx8fI/Xu8+N59+PMvePPoJavYEdXY/EFiO0R++S9v8Ps/a8h9B5TNy/e+y/bxms/+JPDuuz9Hcc5yBUD1w4FNTnxw/jaP/nXZ9S6uAGlq8sNDzr4R+Ns33+dH6w85yS0Z5e3qM96ML3nv6Y8h7X11CGwfr3n5ZkX+vQv+4fE/sfHEB91jkgz8WftLVgJ/+fo/Lr3c5RVgD4958d01F4/L3ye55dP+mPNc0Xvg83xAf5GQtkWbBttuy3u1kltBxXluK86s5tP+GICjsCHJQG6c8M5byMszhl99Cv71w9fiCuger3n+trL9Zg/Ai7zio819XvQtT7tD1nGLnEd81SCHh8gwICLkWhlWIOp8ng94Pr5nrpznChUnN87JOw9YfdwgT359YxD9bWV5C6iUoXWkzqiUEwriRDGO4gUP0xkerTzstnsvlJ8QjED5meRevCBJxqPjAuhyoVB/8yO/nfRrZXjUc3hUFg2wDluO0wVvtU/40/V/Ew57ZMgwDLg5qJIrIbdOnQZWuqXRniRGGzreaT7hj1e/wGtDDCQvl7kXV4AHQSojhnz5i8RotOdQN6gaHrSky+k9FTyAql15z6kk08gAujxkWd4FAsRqoE0DhtB7wFwwL5tNYlRVxo7XhH5Azs8ByDUMK6dNl/3aXOg80HsAwANY1MVObnkLUIjRiHsnmVEMQUe/DlpAkVcJEQEpp+/JiWoYSvbd0gylI4AUS1kSDd2KC9RpoIklC2RXzHcrDhQ/7w8Ttm4glJPNteBtZpUKOJqUZl6U0XtRgEWuuc/XkVuxgKbqqcYYYONx5VEJilOFzLBW8johIYxBEEI70IR+Vlp2md0ouyLqWBQ83OEs4Ap1yFRafLlspnyNjaca1chJsCrMKc0DpJSpQiaPSjO/sjx1cuKOKyDAKnWsYjf/zxAGK6eYEYIaQyvkppiyiGAVrJotbejpPdJ7ic/ZZXQHJUQjtwU0LYUFlleAQFQjjCDI9iJWppi2ipeKL8nOAhSqmIliN34ugIjjyh0PggpN6GcX2BdzYeOROgx0h0J3oBAjqGDJOaq2tKG7hAInRWYERgX4XUaCCETZWcCNXzpZQJSSBimKSyGTJJO/alkLEwKLAyFXoR0tIF8JYipeEGHoR9KDQqAAluC4uqDWgX4P+OQxG9yW3IoF1GGY6wC7soFAKYw8OhYALYHQg7OOW5LmOVjaLW58kluwAGi1o57SIEoQx8QxH6HxDXZsrfGd9v+otS8WYNeXllJmaCBXdzgGuMJBLNVcqQFGACRGRtl4YjAtZe20j6DIauAH7Uc8CKdsPM0usB9LqpjJrZOrXez4unIrLpBkF8xuMmMVL7h+bxMSnLV0VJIx10sIch8XlCyw3HJvpRo8CJvy+2jyUE5ySm8qjscCmqYgGGLmG+GUl9aQkRH/KxhsPLGxhKrN2WMpWd4CKBYQ8C8NYkphdvZPUtWpxKgkX3veXMkoOr2zYGxcvi+g0EhPTzn5Us2N5jzqO2rGascqmfsIIpAEFCPgBDGSGFGLQrJLQYLRsSAleyyz3OUliM184FXJCEEcDyOsHVGdiJPGd+fFie9Bai2uc9ctwEcL2Jf9SD6DoysuEGPmUCMB5yQ3xedHZfQe2FhCxCH6olB4eQUINNqTTWcrUHYKsNHofOL3xiAY1TjQBhXj3Cq2ezhgXxmz5Swky8cAIMlAIBUcsGevvQc6DyUm7OOAPek9MJgyWBj5AJtJEWAsiO6yBUxBUHf1/CRbS5xbzWAB1Jn2tC+9R05zecYQ1IWNJWDED8HvdgyAgvf3ZWJ2sivdtGtlRIPXdzNtPo/cwSWZgNBCSPAW2uPFApLs+IAJD5xbxYm1GIJUGavitY10HthanN8JYRdHqpDRlG+0nFeVZdOgFN9WsWtWMMnkyxKKC7jKJXrLUIY9KHxpseKFDL6LLiCpQlJkZ+G2y+MjmCngJhe+oB7oK4cqISkR9ro+0+lXYUBHUASQNBPTwA2F4ivLcgoIisSIBZ9PX8VLChxPbEqHlQ6kNLBNjkct707U1w0kyrxYNUKwEUAtY7zLKEAEaWqkbfFU2l9BbE6DlwmRcpoCeHSGey0hBJpYWmRBbCZGkxhJd7WBiqPq2F3EAdI0+MEKS04zlsPmQm/FJ1SuE50EZ/sgEdvAKj0ne+kcRM0EceowEGXXYFFK66y79u2vLst5UwgQA1cLwP2NF0ATd+ktOeevRUIXeFRtGMgzUpy4QB37hEkyUTMpZrYLZoGFXKBMhXmd8OjXSMzpxHsLnFtFN0axat3x7Iel5/fXB7/mhXWcWY25omIjYNrxCk0YOKy3vEyLrBpY0gJU8ahzwNtva03BL6NzGqzTUAjTR6VmOAwbzqwor9bSWp+ySEZJZHSKD+KLUWKLKcDrRG4THpwenft7QZw2dCTJnFvF2XCPSgfeefCEdej4w4MPOQwXAPxk+y1OrOXb7TP6ERBBIViyK7Vm1ml799KgaCEoLOkMrfLI4kz+njRjWTjLFW3oeVyf8Hr1nL9Z/4IHoeafL1b85/abZITDsBktpbnEKqmMcwd3MQtYmxhWAdIOBXYWuciJIM6FJLYWZ3eY/PqZBTJbNn5v/L/OjZEJEk8U+zRsteSgzGJB0KoyzirJUBxDuciJTU5zUAsTMGJUgEVOrAI6Nr6LbBMBcjFOiqo4SfKYBvPdhMKIzHX6hOPjGLTa0F8CNFHyPEIzscABZ6VbTqyZ0eCUQmdiRYxa890kRPZZ3n78pdYBC8JRLD2//fZ4o4U2e5qPOLGeIMbDeMqmT7MLpCsMcZIMoSvTYguRIotawL5p5i+x08mck2TSXgv90lDUb+gJLtkyXBAHFAsQcboRvRmlFthaKrl83HijPffCBbX2NFJO/2Vu2HjFaW5mrDCZfqkpwle3zV992cvIfpurtLa++qMLZ7CDyb3HoqgvcfDb6hQvygj56AblpIdxRkBKccOuV5BdC/NL4sQaAE5HKnweobnUWC1FEAbGgoUAt3FfQAonmCibnnqC+3X95BbA7CrnuVDhtY6zBbKDzVEGAsZwCy5wK6TofNJ7SLDWEfujZFN6wpwNwt4702jtJAGbY8dprjkZGrQXsGXg0K0oYJJpI7UONNpzniuGkc3YEkmSWWl3qR02WKAj7kAPjJPjma1FvtiuuGH+6pVl+b6AT1G7RH0TpdGelRYaQ8WLaVuJFYfjbZCpG5TGttqM/vbiRmeRiyFxQwP5lWU5Rig7Yo6b0I2+2oaeqMb9eMZxOC89P09jg6RipR1vVk+pJHNuNRtLs7JUrIzIU+j03gMnfc0XmxbtBF/gugwsqQBzdADPwsbT2AWeuL1yZCpO8DIppvhYH9jcQ7g6VL3PJk3zw+7CktXQMgpwI7zY0DYBeZn4qH9IduFeuCCPfb3904fdGM00+vIitzzr1vQpoNHJope6w72XoBlDXrQcXuSj3BzpesLFgHbCmdX0XoLcZMalDtjdA0g6zGVz75HeSvk7tcWuNkammQMVv4NQ2A053xBfRNCWHzUf0nvg/e3vAvDt9Dkr3c6Pbyxx7jVJBg51Q8D4/uoTvlV/wVq35X9iNNKj2Fw4nQ41n50d3c1y2DcbJCiu9/lBcp7ZOR90HYbyRnzOa8GoRQkIJ3bK05HXmqzg+9Wv6NBCqe9B5EqM10NFksBP6+f8TN+4gwpwx7clzR3/h/Duv/89fQ6cPl8B0BxsqdOAqqEC3RDo+oj75d6oexmW0r02mYjTVj0qzpOP71M9iTz4L4P+8hTKq4osentcpNwdPj4qf487LFdcZPdjhvTD5ZufIeyeuyqhxA15eYafnOJdh202iyx5WSDkjp2ezbdBgUJcyHjBYbpZPgzYMIDtTF1SLKPzUJ7Zu1Q53Q+yszNsvGW2lPw/dEpAZKjQIYkAAAAASUVORK5CYII=\" y=\"-34.925311\"/>\n   </g>\n   <g id=\"patch_33\">\n    <path d=\"M 465.522642 98.925311 \nL 465.522642 35.7555 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"patch_34\">\n    <path d=\"M 528.692453 98.925311 \nL 528.692453 35.7555 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"patch_35\">\n    <path d=\"M 465.522642 98.925311 \nL 528.692453 98.925311 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"patch_36\">\n    <path d=\"M 465.522642 35.7555 \nL 528.692453 35.7555 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"text_7\">\n    <!-- trouser -->\n    <g transform=\"translate(475.665985 16.318125)scale(0.12 -0.12)\">\n     <use xlink:href=\"#DejaVuSans-116\"/>\n     <use x=\"39.208984\" xlink:href=\"#DejaVuSans-114\"/>\n     <use x=\"78.072266\" xlink:href=\"#DejaVuSans-111\"/>\n     <use x=\"139.253906\" xlink:href=\"#DejaVuSans-117\"/>\n     <use x=\"202.632812\" xlink:href=\"#DejaVuSans-115\"/>\n     <use x=\"254.732422\" xlink:href=\"#DejaVuSans-101\"/>\n     <use x=\"316.255859\" xlink:href=\"#DejaVuSans-114\"/>\n    </g>\n    <!-- trouser -->\n    <g transform=\"translate(475.665985 29.7555)scale(0.12 -0.12)\">\n     <use xlink:href=\"#DejaVuSans-116\"/>\n     <use x=\"39.208984\" xlink:href=\"#DejaVuSans-114\"/>\n     <use x=\"78.072266\" xlink:href=\"#DejaVuSans-111\"/>\n     <use x=\"139.253906\" xlink:href=\"#DejaVuSans-117\"/>\n     <use x=\"202.632812\" xlink:href=\"#DejaVuSans-115\"/>\n     <use x=\"254.732422\" xlink:href=\"#DejaVuSans-101\"/>\n     <use x=\"316.255859\" xlink:href=\"#DejaVuSans-114\"/>\n    </g>\n   </g>\n  </g>\n  <g id=\"axes_8\">\n   <g id=\"patch_37\">\n    <path d=\"M 541.326415 98.925311 \nL 604.496226 98.925311 \nL 604.496226 35.7555 \nL 541.326415 35.7555 \nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g clip-path=\"url(#p150a03b7cd)\">\n    <image height=\"64\" id=\"image09892cf9ae\" transform=\"scale(1 -1)translate(0 -64)\" width=\"64\" x=\"541.326415\" xlink:href=\"data:image/png;base64,\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\" y=\"-34.925311\"/>\n   </g>\n   <g id=\"patch_38\">\n    <path d=\"M 541.326415 98.925311 \nL 541.326415 35.7555 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"patch_39\">\n    <path d=\"M 604.496226 98.925311 \nL 604.496226 35.7555 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"patch_40\">\n    <path d=\"M 541.326415 98.925311 \nL 604.496226 98.925311 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"patch_41\">\n    <path d=\"M 541.326415 35.7555 \nL 604.496226 35.7555 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"text_8\">\n    <!-- coat -->\n    <g transform=\"translate(559.911946 16.318125)scale(0.12 -0.12)\">\n     <use xlink:href=\"#DejaVuSans-99\"/>\n     <use x=\"54.980469\" xlink:href=\"#DejaVuSans-111\"/>\n     <use x=\"116.162109\" xlink:href=\"#DejaVuSans-97\"/>\n     <use x=\"177.441406\" xlink:href=\"#DejaVuSans-116\"/>\n    </g>\n    <!-- coat -->\n    <g transform=\"translate(559.911946 29.7555)scale(0.12 -0.12)\">\n     <use xlink:href=\"#DejaVuSans-99\"/>\n     <use x=\"54.980469\" xlink:href=\"#DejaVuSans-111\"/>\n     <use x=\"116.162109\" xlink:href=\"#DejaVuSans-97\"/>\n     <use x=\"177.441406\" xlink:href=\"#DejaVuSans-116\"/>\n    </g>\n   </g>\n  </g>\n  <g id=\"axes_9\">\n   <g id=\"patch_42\">\n    <path d=\"M 617.130189 98.925311 \nL 680.3 98.925311 \nL 680.3 35.7555 \nL 617.130189 35.7555 \nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g clip-path=\"url(#pace5a6b5f5)\">\n    <image height=\"64\" id=\"imageeb5439a88a\" transform=\"scale(1 -1)translate(0 -64)\" width=\"64\" x=\"617.130189\" xlink:href=\"data:image/png;base64,\niVBORw0KGgoAAAANSUhEUgAAAEAAAABACAYAAACqaXHeAAAVAElEQVR4nNWb2Y5kR5KePzf3s8SWmZWsjUsXyRG7Z6CRRoA2QAJGF4Kga13oIfQ4eomBXkDQpTAYSIAAaUbTwPQs3WqyyeZSVVm5REbEiXOOL6YLP+dkFEmRmVXZF3IgEFUZER7uv5v/Zvabhfk35t8rv8Mh8znhn/w+7aOS5qHQnRpOfhVZ/ue/RLvud/nVtxruTu8Wi7EWYwWsBVW079GkkOLN+4wBvcHVxIR4BQMq+fGD41uf/77XjStADMba117SGFEf8n8O1/T/GLcHQCz2k4/o3ztm/7jk+iPBNfDofze4ix1885J4tc6LNwIkUCW1HcWnzymez9h88C7Nx55yXXBkLYdbNM6BtRjnMGWJ9j2pab4XCPf0Cf0n7+JXju17jlgZJCgmweKbyOKzNWbbEL/6Bg3hHgAwGenwcMn1hxXbZwb9ow3bdcXsrGYhhvLyGlgffEZAI6RIeP4C4xwq77F8uMOvTrIFHQ5rMWWJKQtMWYIY2Ld5jm8NXS3YfFjRnQjrn0V0HsELJhj83OGaJWVh4flLeFsAZD4n/LPfZ/+o5OoTS/NBxJ52/KOn3/DV8pirT57QH9U8Pj+G5y+GE0vfXXRSjn/jefm/Tjj+IiFHK4wV4vU2m2qMaN9j5jP0aIlpO8z6Gv3uVPjHK87/oSEcB548u2BVdazbmtY7NnJEqiqWXxY8+LQi/gjP/CgAZrXk6385o/mk5/33z/jXD3/L43LDx9UZvzx6yp/84YruYc2Dv10wXe3vu78psvir5zx7eQIhoUeLfNr7Fu1iNtUQMEVBeDDHXstwlb47mncrPvqnX/J7q1f889VnrOye/9M+5dwv+LuTJ/zm3VMu/uaI0/86g+vrtwNgGgoKBLU0seQqzvHJMp93bI4c/XHB4uQYM5uhi1nmAhEQg1YF6oT9SYU/cqgBtQbxSv3BKeIjJmTO2L8zY//QUTQ189lPkS5iVDOoIWJCpDsRlighWdZxTqsF6zhjGyt8tBn/W/q2HwcgKbYH01rWzYyvZ8dcFzXbWNElxyenr3g1a7l+9i6znz5j98Gc7fuWZCEV+dG9k0i15puhBp1HZg/2qBq6pkBDiWktxhtSlaAK0AvF5REmgHiDiVDswDXK5vdgGR1n3ZK/SM8oJLLua9pYcLZb0O5Lqs6gP+RJbg0AZDT1xrKDCvtYktRQ2sjMec5PYPfBnN1ToXlXs7szoIWS3vG42hODRaOhnnueHG9QNexmJSEKbV8QvKVwEecSfefwpoRoMNFAMoSdwe0MYRHxSdiHglICQYU+OWISYhI0mu+joTcHQAXUKou654P5FV6FfSzok+OyndMny4N/9Zzwx4ZHZccf1DuaUHLRzolqKCRhJRGTkNQAEJMgRjmZ7bEmcVo1LFzPZT/jvF3g55ZmUWCMclR3VDaw6SuarkSi5exyRVEGZg89pXTU1lNKwNk5qMEokO7LAgwgUNjIwnXsY8E+FiQ1dDFP8S8ef8ZPZy8oTUBIXMQlv2yeTu8N6YbQmlBy3dWIUWbOU1vPR/NzHpfXfLp/RBsLghUqFygk8pPFFUduz3WYsQslX2we8PXmBFUzAeokTf++Xw4QkyM3UazJsx65lsflhld+ydfbY/be8Yv1u3zTHiMmUUlkHwuu+hkhCT5lnz9ulmF9SQ2CIkYpJFKYyCbUfLU+zrgbxYrSRUcpka0vab3DR0s199Sl51G9ZeVanrdHrLsZIVqMGQ/N3AMAMAHAsOiVa/mwfEUlnv+RPqLtC75aH/NcVjibzR2ymQOoGozJGx0BeA1jFEEzAL5is50BYF1ERNm1JcYofVcQveCqwGrRclR3PCy3LG3Hl80JO1/m7xRFf3zvtwdgvAJilKQGr5ZWC3xyGKMZcZg2Wdk4mSPkz43/G8nKDH+HTKrnfoFXSxNKRBJGlLIMmOE9I4j5e6B0kVIiXi1dcqQhwcjrUfixfOO2ABhjUAvG5VPtUjHFAU0qEaOI3Jy6k0TtPKoGGfihsnkjUYWdz95DhoUmDH10fLE7BeC6qymrgJXEvOoRo/TBEZOhMw7UIKIsy57KBfrkSGoIhwBY/fGE67YAAJMbBBCTN5o0M7o1ihs2n5Lkt6ohcUNQQQUhW8/4NysJMUoYvEGeO1uQPQA0fcuWVfMmKxsoJeCTJZobIjQw+N97AkBVkQDaW5IaZtZTDKYHcFLvKWzkspnR+TzduOSR6Pa+IGk220ISxRA7hCRs+4qkhmXZMXc9M+dJtSGqoQ8WVTOxfUoCQRBRntQbnEQ2vp5Ov3YemJFCDpzuBYBpJ99CNKpMpDa6uJTyQuPBqakaYjLDc/Ykh6ca1UxkCTc88lqqbPS1K22MsnA5yblUoR+u2mhJmgwmcc9xANBHy5WfsbTCvOyZS8/Hi3P2seBiP2cTLJRhIsE2DBxQBMQoMQl77+iCY++LabOljaianGf4kvW+xtnIovRYSdMV64Nl31sWVc/T8hqvli+bE9ropuuT1EAvyL1awDB8FHahopKAmERhIrV4WltgJWVzJQdMOSnJyFU2YiWx60tCtANPOJwkFlU/3feQhD5a+uEqzVxDYSOlhAHIOa1NlDZy7Bq6VOR1JUshMQOQzBA+w/fm0m8CQDYnM22oS45Lv6CQyLHNoayByU35aDFGWVbddDLCTRyR7zUEhKgGSQLDBmoXiLMOZ+MEys7PiUnovMtXaXTFqZi8gBjFmURKgnSCeH5YVrs1AClncSaaKZbfx4IX6YiF6zi2ewoTMwOPfj3lMPZB1SBGc2h7wBOqJp8UiRCHKJF8h5dlN5BZ5g+fLFcDwfa9g5TX0aWCTnNU6KNlUfSUNpCSwXYG293ODdzKAiQCw51yw+XKbk4oTETMzebTYMYAjS0nVzcyefZYisggH46fG9zmaCFBhTbkDXbeEYKgycBoZWqJB85+tABVMJF7JEFNiAfpshEvXU8XHbtYUkpgdWABSQ19b0mpwtpEF9xEYmI0B0cDV4BOJDiS1wQUhsYXnK8XpGhJwWRml6wsJzW0aUjIMJM3Wrg+g9FlseX+OCAqJmaTPLzLSYW5dNTGTzwA2cyNye4PBDuAk5SJR1IyWDuEtsN8cQiuprw+CarZrTGCJjrNB0zrGRMq1CCBe4wDVJEebGfwPsfdXXI0oeS4aPlZ8ZJj8ZxWDZ8XMS94MGM7LBaGoCgJMQ6PYBEbqYvM8G3IBOej5IAHWC33hCQ0u5o0JEF17TkqOwqJRGTIGBMz61nYjhiExRrKbULjfVhAUiSCBAhDoBNUJlI7Fs+ptcysRySzcBq+d7zfN1Nl8ktRSMEAdnKdIVpiytcgRsHaxKL0uCTsjYJRnEvURaBy4bWTH71MYSKaMgHaLjEt5K0A0JQn3Bv6aLOIYT1SKUduT6tCM1RgRJQYGUAYM7ebyM9KwrmUCz/D4rtgp0gRbqxE1dCQE6fQ2awRHkfeW17zZHbNyrYAlBKzd8LQpBLdO2YXiXLtId5HZSgptldcCykaxCRmElnYniPX4hE2mrO6KXWNhiTmNYIb09nCxgxKGhKlYIkmn7ghu+4UbwCMUaDLgmnpIs8WFzwoGubS4dXiJGKMwydLnxyyF6rLHrtp70cUVdXBpBSSoTA3ZtUlx9fhmNp4jos9HxyvebFdsvYOkZwlOkk0viBEmeKIfMKjcp7T6ZFAjQGxaXpNVcEqmiAmwyZkKe3Y7mlSSRuLSXESMlnbfcC0Ptcs3xYAYqTYBcprQb0wsz1eM9pNKvn5/hmVeJ5VF7z/6JL/Lp9wvZljbWJWeARlva/pg83p8ugJ1GCGtNcNuYCqAUmYIrtIO2gNXZHQgXfO2mWuR0jPdZix7Su6mDNVJxEJBnvZYHZ79F6uACBdxHUJ4k2wEqdnIalQiacwkbnrsS5i7eCqzOvh7+HzeA0OU97xzAzgbIKYJS4MzEvPk3rD7Fuy2qH+YCKY3qP9d6W3NwJAfcBdNtSA7Gd4tQS1hGRzajsY7zt2y8rueX92xedHD6Zs8DC6y4sFTUIMglhDHAg0DMQHmTidSyyrjpiE7a4mJsM/fvQl/+HRn/Ibf8qfNx9Pa5yiSRVsa0gvX+Xeg3spj2vCtD2yLzEhBz9xQPxwiEnUxlNJoJCEH4KZmORg88Mz5OTKaAaEG4s4DI/FKDq6UqOsXMtPbGKTtpMbHMekMSTQrvvRsvitAdAY0fNLbNthuyPEJOyweZ9yRgawiTMsylftCc+vVnnyQUdUZQiBc3THcA0OpVuRfO9jFHzviFF4pQtiEkLjwAu/3j7kf66OuYhLVralcWWW1oaYxKtgfpz37gYAqsTtDgkB8d/Vmr1arCZaLdilknVf0zclGCVVARHNLs68NuV3xqgux5hd3xg0aRLoBeOFy27Op/1jgEmLcEMIPobmt9UCbw8A5KTiIKryybKPBc6kXPjAsg5zGsnZXznvEVFmVY+YrOQo4BwkOZCtRAfTl9eiRmOy+de1z4HR3KIhcVzuObENu1SxSTVNLOmTJSRh3c9oY5F1gPsHQHNcPawxqaEJORv0ahFVzv1ievuDVUMhiaOqJanhYj+n825wezdz5KkNKTGkxzcgiM1qEeTEKUbhUb3l1G7p1bKNNftY4qOlD5aLmIsprv1dADAME2ET6qkuOGVkJsfhkKXpmCT78IMNje9PaXB/epPbZyBgFB8nYSVakg5XIuY5F9JRmkibCrrkiDoKrhlZuWVV+I0AsB18uTvJX0QWIMa7uLQttQl8sT+lHaSrcYwRYN+7XCIfVGYjZooIxwwQ8ukDbPcVKRn6poAhZnhqG87iEZtQs/Y5wPLRZpCSYRnuRgJ3AkAiNL6gkERlAwlDRIgqWJRquIAxCsEo3aAKxzQSmvlejeIwRjj8W0o5ZiDl/oB9LDhPFbtU5fUcWFCMhhTtrXWAcdyhRSZhW+VyO6cuPcczxabEPhZYEjgoTaCNjrYp6YSpUJLlLJn0wDxf3nA0FiNpyAmU4C0xDNaQNRAYGiT+8sX7/Mf4b1m4npOiIRaGWRFISdjtZpidpdzprXKAuwNA1gT63g1JSr7TfXJ0BwlSUkMKgjHQaZFT3+Hlw82PCk8OioRRyFc1aJTMDyMAw2PXVPzq8hFPlhselZscdA3ZJV6wjWD7u5HAnQBweyVelbQnUBxnGXvjq0mbW9hjxCiPHl/TeUfT5DucQjZjUyRkyOw0DmafDIhO1qFqBvETjB1AKBOoIQbh8nqOMcr5fJFzEBuoioDdWOYvDOU63EoLvDMAmhTXKu7aEiqHNUOqG0oA2lAgJhctfvbgjLP9ks/2ZT7pINmMXcqqEZL7HzW3xGoyKPnUNZH7e+RAOC2zIhRbBzvh2kXWJzOEXFWqbKTYGWZnieK6v1MsdCcLKK8C8xcV2+qG5cdEpx2KEyMgfbIsFy0+WvZWSUEQlwbT1hsXaLiJEo1iBIxTjE3YIk66wDiSy+LJdZ91gS462uBwO6gvIrLruAsP3okE66+ueUeOSUVJ+qOh2WnI00dX1wVHFyzz0vPRyQUA3+yOaPuCPlhCyJmEkZtmh/ysw2YTlIpzkXndYw82H6vs760kznbLSVbfdSX1K2X+2RVcrG9VERrHLdsIAFVM01JcdUg3xgE3xBUHXT8Mqm8fsmgSNKfNhYuvNYGbw0bQwSJEFLEJa9NEtDFlOT19y0XGNMruedheMfsObqkDjONOVyA+f4lcXFH/wT9gVbasii7X55PQdOUUkMSQQfhtOsmdHqVnUfbs+yLL4aK4IkyEp2pwLuFcnFSgEC1NW6LKIKTqRJJmAMtKVonFKEWjxG9e3LTK/y4A0K5Duw7b3yi9o4oTB61vqv1FofcuS2Pld09lzPtjMlNfjyFfgcJmeX0EJyXNrTqDkiQCowsdr554faMfYNztBxPDKLaJv3nxlOWsuyl8HKg51sXp3ykZrnazyfyr2g/JzXj5dSK6sXwWkyCSmM2GZOigmqRqsDZRuoCPlpdnR7AteLK7Yxr4NgDYNtJe1QRvWS33WbsbxiFjw1Dh7bPXKMpAWYTMEyELKWaoKk/dXTBZxbzqUc2tMrn9LYPoJFG63IPApqBYC9LeMQZ+GwDMGJ6ShcpZ4W/i/YM0d1KAhw+pDiY7NFNlk9fBOm6Ko68XUW/mGCWywkWOyo6mK6mfW2Znirtu7+T+3gqAXOjLTyf1nqOipfEFfXCkxCR/p0Ohc9jIyBPjGF1f/psZKkMGESFO9YE09RUao8wLzzv1jpfbJasvlNUXHeZizWuTwj01SHzPKNYty8+O6U4tX8xPOJ3viUmoizC4J5mKoSkZQsg6oBkYHgTVvLns8tLUYOGjBdzE9GKU0sXXGiav24qL5imbF0ueXUSKyz3a92+ylTe8Ar/4NT/5YkX68Amf/bsTPn+85MHTa56uNlOdbiQuHy0Xuzl9b7GDj7c2URTZKqoiN0UeVy1z13PRLrhsZkPBJBdOlmVPIZEu5oaJ3359yuqvKh6fKcuf/5b48ox06P7uEAi9EQCpbaFtcXVF/eoItY71fI4Vxdk4qUFjvl7YCCXfqQIdNkiMj8oGZqW/qQwNtUWfLJfNjN2+wl4UzM6U2auAbndv9fvDN+OAEYizV7z/XyrSqmb78ZL9ac36sWH/XgCnmCriysgnT894MtuwCyVNKNmHgqt9fTOPGrZ9ReNLjqqWP1w8J5Fb8ZtQ8qtXD9nvKlZ/XvOTn7e47Rb76hrajrjZvM0W3hKAtoVf/hrEcrT/e8weLbjyNbG2pALiXPBzYVl0fDQ756xfcWHmQ1fX63P5QRJzJvKw2hKScMGCJpS0+xLWBSefBuyf/RzVRLiDmf/QeCsApqEJXp5T7va8c33E6qsZyRpSaWhPCv72nce8N1vjVZhZz7XJWh6As2BFWZS5TVaM8qpb8hcvPiD8t1PKa+XppeL2icXfnRM13emO/9i4JwCU+OocXp3D51AcvLT66Bl//cePWT+d4Uyauszi4CrzyL3Dp1VDnyzn3YKrr474+//pc8KXX3Hzrvsft88G33T4gL1y/OL8KS/aoWRmcn5Ql55l3bEoPcuiY2Y9TSj57fUxbmNv1eHxtuN+LOAHhvaexZfCq+Idug8dj6ottfOczhog/4ymtIGH1Y6F7fjryydcfHXC8pVBw/9vAHzfr741YYYmq8Mu8dEFuqFFdvztUFKTO1MTd9L23nT8X5DmRUgdi3OyAAAAAElFTkSuQmCC\" y=\"-34.925311\"/>\n   </g>\n   <g id=\"patch_43\">\n    <path d=\"M 617.130189 98.925311 \nL 617.130189 35.7555 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"patch_44\">\n    <path d=\"M 680.3 98.925311 \nL 680.3 35.7555 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"patch_45\">\n    <path d=\"M 617.130189 98.925311 \nL 680.3 98.925311 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"patch_46\">\n    <path d=\"M 617.130189 35.7555 \nL 680.3 35.7555 \n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n   </g>\n   <g id=\"text_9\">\n    <!-- coat -->\n    <g transform=\"translate(635.715719 16.318125)scale(0.12 -0.12)\">\n     <use xlink:href=\"#DejaVuSans-99\"/>\n     <use x=\"54.980469\" xlink:href=\"#DejaVuSans-111\"/>\n     <use x=\"116.162109\" xlink:href=\"#DejaVuSans-97\"/>\n     <use x=\"177.441406\" xlink:href=\"#DejaVuSans-116\"/>\n    </g>\n    <!-- coat -->\n    <g transform=\"translate(635.715719 29.7555)scale(0.12 -0.12)\">\n     <use xlink:href=\"#DejaVuSans-99\"/>\n     <use x=\"54.980469\" xlink:href=\"#DejaVuSans-111\"/>\n     <use x=\"116.162109\" xlink:href=\"#DejaVuSans-97\"/>\n     <use x=\"177.441406\" xlink:href=\"#DejaVuSans-116\"/>\n    </g>\n   </g>\n  </g>\n </g>\n <defs>\n  <clipPath id=\"p0230cb50fc\">\n   <rect height=\"63.169811\" width=\"63.169811\" x=\"10.7\" y=\"35.7555\"/>\n  </clipPath>\n  <clipPath id=\"p5c162b7883\">\n   <rect height=\"63.169811\" width=\"63.169811\" x=\"86.503774\" y=\"35.7555\"/>\n  </clipPath>\n  <clipPath id=\"pf2dc3baed1\">\n   <rect height=\"63.169811\" width=\"63.169811\" x=\"162.307547\" y=\"35.7555\"/>\n  </clipPath>\n  <clipPath id=\"p7ae8487890\">\n   <rect height=\"63.169811\" width=\"63.169811\" x=\"238.111321\" y=\"35.7555\"/>\n  </clipPath>\n  <clipPath id=\"p4827dcf0cf\">\n   <rect height=\"63.169811\" width=\"63.169811\" x=\"313.915094\" y=\"35.7555\"/>\n  </clipPath>\n  <clipPath id=\"p0ed67412e3\">\n   <rect height=\"63.169811\" width=\"63.169811\" x=\"389.718868\" y=\"35.7555\"/>\n  </clipPath>\n  <clipPath id=\"pc5ca76c84f\">\n   <rect height=\"63.169811\" width=\"63.169811\" x=\"465.522642\" y=\"35.7555\"/>\n  </clipPath>\n  <clipPath id=\"p150a03b7cd\">\n   <rect height=\"63.169811\" width=\"63.169811\" x=\"541.326415\" y=\"35.7555\"/>\n  </clipPath>\n  <clipPath id=\"pace5a6b5f5\">\n   <rect height=\"63.169811\" width=\"63.169811\" x=\"617.130189\" y=\"35.7555\"/>\n  </clipPath>\n </defs>\n</svg>\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  }
 ],
 "metadata": {
  "orig_nbformat": 4,
  "language_info": {
   "name": "python",
   "version": "3.6.8",
   "mimetype": "text/x-python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "pygments_lexer": "ipython3",
   "nbconvert_exporter": "python",
   "file_extension": ".py"
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.6.8 64-bit ('torch': conda)"
  },
  "interpreter": {
   "hash": "2b23fc4612d2a20491382ea15c73e42619afc437d67ed04c4074dd835ed863ea"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}